Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T00:41:45.400805Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2608.00129.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T00:41:45.400805Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
49 of 49 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 54d30d06-2e1a-4f50-8a32-23f85b8719b7 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Maximum likelihood estimation of intrinsic dimension,
Reference 1
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Observation 29ece130-2877-44a6-b02b-1234ae9b39a4 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Estimating the intrinsic dimension of datasets by a minimal neighborhood information,
Reference 2
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Observation 25f6940a-6c0e-428c-9e09-f32dd68950bb · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Intrinsic dimension of data representations in deep neural networks,
Reference 3
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Observation bdc76e4b-8641-430b-9016-aa33edab9801 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Rethinking feature-based knowledge distillation for face recognition,
Reference 4
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Observation 48b77857-2091-4507-98c8-9129c4152d58 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Understand- ing deep learning requires rethinking generalization,
Reference 5
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Observation 7dda9964-5885-400d-bff1-b60be9e45c53 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Reducing Overfitting in Deep Networks by Decorrelating Representations
Reference 6
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Observation 58c69924-75d9-4e4b-a4bc-515c942ec7e7 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Rethinking the value of network pruning,
Reference 7
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Observation 45e9a17d-7924-464e-a35c-f35a08211f40 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Separability and geometry of object manifolds in deep neural networks,
Reference 8
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Observation a8733015-ea0e-4630-aec8-c0bfda921dc0 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression The cityscapes dataset for semantic urban scene understanding,
Reference 9
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Observation 42877d43-5794-4e0e-adfb-24ae6d70e468 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,
Reference 10
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Observation cd7895d0-b1ce-4f2e-8de5-f244d387d43e · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Deep residual learning for image recognition,
Reference 11
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Observation 44bdb5af-71f4-4d98-a10b-e43596ca69e5 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Learning multiple layers of features from tiny images,
Reference 12
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Observation 0e816b68-970f-4911-a5b8-0d430d0b3cd0 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Pruning filters for efficient convnets,
Reference 13
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Observation 0ffa45fa-ac20-445a-85b7-5254302d442a · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Distilling the knowledge in a neural network,
Reference 14
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Observation 373c1a3d-c105-4419-af27-5063b675503e · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Improved knowledge distillation via teacher assis- tant,
Reference 15
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Observation c79ff685-4b04-4198-836a-b11fc996e34b · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Knowledge distillation from a stronger teacher,
Reference 16
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Observation 3fd47dc6-395d-420e-ac95-b9205d43335e · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Conflict-averse gradi- ent descent for multi-task learning,
Reference 17
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Observation fd44600d-a514-43ac-ab1d-bc1b4840391a · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Patient Knowledge Distillation for BERT Model Compression
Reference 18
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Observation 9030a84c-9a0b-4b18-bdc3-45a0dc5d431f · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Curriculum temperature for knowledge distillation,
Reference 19
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Observation 77ddb675-da69-4a63-9d3d-618c68925add · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Rethinking soft labels for knowledge distillation: A bias–variance tradeoff perspective,
Reference 20
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Observation b55e39f0-b369-4498-b6de-9205450a91e8 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Controllable dynamic multi- task architectures,
Reference 21
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Observation 8e0e6295-45e0-44b0-93c2-fb9a94c6e9a2 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression GPT-4 Technical Report
Reference 22
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Observation 1e1bafa9-c788-4ad8-a806-f43d9475171a · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression DeepSeek-V3 Technical Report
Reference 23
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Observation 7802ae2d-e4a9-49ff-947a-ac1424018c3f · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Student customized knowledge distillation: Bridging the gap between student and teacher,
Reference 24
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Observation 0e4e2623-3070-4c1e-8f09-d624f806328d · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Gra- dient surgery for multi-task learning,
Reference 25
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Observation b2b3ec1b-8d84-4a5b-b008-30184620d1dc · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Famo: Fast adaptive multitask optimization,
Reference 26
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Observation f0c87c78-f12d-4320-8373-6aa39e099c55 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Knowledge distillation for multi-task learning,
Reference 27
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Observation b4fd4dcb-10f9-4bb4-ac47-196bacfc23a7 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Contrastive representation distilla- tion,
Reference 28
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Observation c6c8d4a5-e74f-42c5-b6be-319e2b6f79a2 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Cross-layer distillation with semantic calibration,
Reference 29
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Observation c966cd39-c4df-4366-b0ac-937dd634ee5f · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression On the efficacy of knowledge distillation,
Reference 30
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Observation ca0ad48d-4f1a-4f6e-87fc-6e37d5bb778c · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Segformer: Simple and efficient design for semantic segmentation with transformers,
Reference 31
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Observation 42ca9a07-8e5e-4dfd-9024-692f4cf1110f · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Reference 32
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Observation 66964b90-1d97-41b3-8022-b9ebe4035c7d · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Reference 33
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Observation a392c23c-ce01-4817-bcbd-cb71c25b26cb · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Tinybert: Distilling bert for natural language understanding,
Reference 34
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Observation f03b68cc-c611-45e2-ba1c-45702754f605 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Attention is all you need,
Reference 35
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Observation 6fef3917-e48c-48b8-9321-6fe7bd0752a2 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Learning multiple dense prediction tasks from partially annotated data,
Reference 36
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Observation 67fea1e5-6302-4981-91f4-57d8090260ad · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Does knowledge distillation really work?
Reference 37
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Observation ca4ff926-6eff-49ed-824b-6aa7d5c57072 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression The platonic representation hypothesis,
Reference 38
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Observation 235a84e6-e2e1-4861-8819-7fc7b363a5d4 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Densely guided knowledge distillation using multiple teacher assistants,
Reference 39
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Observation 62b3711e-258e-431c-90dd-e675931152a3 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Monotakd: Teaching assistant knowledge distillation for monocular 3d object detection,
Reference 40
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Observation 245f2a91-cea5-4728-829c-5f9810986897 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression An overview of statistical learning theory,
Reference 41
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Observation b3e74537-92e8-4095-9887-e29665bada42 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Unifying distillation and privileged information
Reference 42
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Observation 51c7dd8b-cb5a-4686-abd5-5a7c45066fa5 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression On the difficulty of training recurrent neural networks,
Reference 43
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Observation ff3e5545-6769-478e-9994-bc2755430aaa · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression An Empirical Model of Large-Batch Training
Reference 44
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Observation f5941856-39a2-4413-b3b9-d8d2c1d858bc · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks,
Reference 45
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Observation 707b3328-6f5e-4522-bb94-1ef4f70af353 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression A Study of Gradient Variance in Deep Learning
Reference 46
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Observation 123b12bd-af4b-4ec0-b70c-5fe2a9797b6e · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Decoupled knowledge distillation,
Reference 47
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Observation 3836c3af-2be0-4d7d-8bfb-615999f8f3aa · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression End-to-end multi-task learning with attention,
Reference 48
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Observation 011fa260-d639-435b-8e6f-7e1305d9a681 · outbound
Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression Inverted pyramid multi-task transformer for dense scene understanding,
Reference 49
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No inbound Pith citation observations are available.